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Updated in [February 21st, 2023]
This course provides a comprehensive introduction to using Torch-TensorRT on Nvidia GPUs. It covers everything from setting up a Docker container, installing Nvidia Container Toolkit and Nvidia Docker 2, loading ResNet50 and a sample image in Pytorch, training with ResNet50, using the softmax function, and mapping ImageNet class number to names, to benchmarking functions, running CPU and CUDA benchmarks, tracing models, converting traced models to Torch-TensorRT models, and running TensorRT benchmarks.
Possible Development Paths: Learners of this course can use their newfound knowledge to develop applications that use Torch-TensorRT on Nvidia GPUs. They can also use their knowledge to develop applications that use other deep learning frameworks such as TensorFlow, Caffe, and Theano. Additionally, learners can use their knowledge to develop applications that use other GPU-accelerated libraries such as cuDNN and cuBLAS.
Learning Suggestions: Learners of this course should consider taking courses on other deep learning frameworks such as TensorFlow, Caffe, and Theano. Additionally, learners should consider taking courses on other GPU-accelerated libraries such as cuDNN and cuBLAS. Learners should also consider taking courses on computer vision, natural language processing, and machine learning. Finally, learners should consider taking courses on software engineering and data science.
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